For the EndoVis2017Challenge and EndoVis2017VOS experiments we compare the proposed approach with the unsupervised Anchor Generation and Semantic Diffusion (AGSD) approach ([1]}), based on handcrafted features, and with the fully-supervised state-of-the-art approaches TernausNet-16 ([2]}) and MF-TapNet ([3]}). Results on EndoVis2017VOS for these approaches were obtained by training the models using the code publicly released by the authors ([4]}, [5]}, [6]}). Additionally, we compare our results with BaselineFS, a model sharing the same architecture as the Student network (Unet16), but trained in a fully-supervised way on the GT labels. We do not provide fully-supervised results on the STRAS dataset, due to the lack of GT training labels. We also do not provide results for the unsupervised AGSD approach, due to the fact that the handcrafted cues selected by the authors are specifically tailored for the EndoVis dataset, yielding poor results on the significantly different STRAS dataset.
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